





'''simple docstring'''






import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase									=   get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class 		lowerCamelCase	(   _A   , unittest.TestCase							):
	snake_case_   = SpeechTaTokenizer
	snake_case_   = False
	snake_case_   = True



	def 	_lowerCamelCase     ( self						):
			super().setUp()

			# We have a SentencePiece fixture for testing
			lowerCAmelCase  :     Optional[Any]       						= SpeechTaTokenizer(a_						)

			lowerCAmelCase  :     str       						= AddedToken("<mask>"		,   lstrip=a_		,   rstrip=a_						)
			lowerCAmelCase  :     Optional[Any]       						= mask_token
			tokenizer.add_special_tokens({"mask_token": mask_token}						)
			tokenizer.add_tokens(["<ctc_blank>"]						)

			tokenizer.save_pretrained(self.tmpdirname						)



	def 	_lowerCamelCase     ( self		,   a_						):
			lowerCAmelCase  :     Tuple       						= "this is a test"
			lowerCAmelCase  :     Tuple       						= "this is a test"
			return input_text, output_text



	def 	_lowerCamelCase     ( self		,   a_		,   a_=False		,   a_=20		,   a_=5						):
			lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= self.get_input_output_texts(a_						)
			lowerCAmelCase  :     Tuple       						= tokenizer.encode(a_		,   add_special_tokens=a_						)
			lowerCAmelCase  :     Dict       						= tokenizer.decode(a_		,   clean_up_tokenization_spaces=a_						)
			return text, ids



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= "<pad>"
			lowerCAmelCase  :     Tuple       						= 1

			self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_						)		,   a_						)
			self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_						)		,   a_						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[str]       						= list(self.get_tokenizer().get_vocab().keys()						)

			self.assertEqual(vocab_keys[0]		,   "<s>"						)
			self.assertEqual(vocab_keys[1]		,   "<pad>"						)
			self.assertEqual(vocab_keys[-4]		,   "œ"						)
			self.assertEqual(vocab_keys[-2]		,   "<mask>"						)
			self.assertEqual(vocab_keys[-1]		,   "<ctc_blank>"						)
			self.assertEqual(len(a_						)		,   81						)



	def 	_lowerCamelCase     ( self						):
			self.assertEqual(self.get_tokenizer().vocab_size		,   79						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     str       						= self.get_tokenizers(do_lower_case=a_						)
			for tokenizer in tokenizers:
					with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
							lowerCAmelCase  :     int       						= tokenizer.vocab_size
							lowerCAmelCase  :     Tuple       						= len(a_						)

							self.assertNotEqual(a_		,   0						)

							# We usually have added tokens from the start in tests because our vocab fixtures are
							# smaller than the original vocabs - let's not assert this
							# self.assertEqual(vocab_size, all_size)

							lowerCAmelCase  :     List[str]       						= ["aaaaa bbbbbb", "cccccccccdddddddd"]
							lowerCAmelCase  :     int       						= tokenizer.add_tokens(a_						)
							lowerCAmelCase  :     Optional[Any]       						= tokenizer.vocab_size
							lowerCAmelCase  :     str       						= len(a_						)

							self.assertNotEqual(a_		,   0						)
							self.assertEqual(a_		,   a_						)
							self.assertEqual(a_		,   len(a_						)						)
							self.assertEqual(a_		,   all_size + len(a_						)						)

							lowerCAmelCase  :     Tuple       						= tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"		,   add_special_tokens=a_						)

							self.assertGreaterEqual(len(a_						)		,   4						)
							self.assertGreater(tokens[0]		,   tokenizer.vocab_size - 1						)
							self.assertGreater(tokens[-3]		,   tokenizer.vocab_size - 1						)

							lowerCAmelCase  :     Optional[Any]       						= {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
							lowerCAmelCase  :     List[str]       						= tokenizer.add_special_tokens(a_						)
							lowerCAmelCase  :     List[Any]       						= tokenizer.vocab_size
							lowerCAmelCase  :     Tuple       						= len(a_						)

							self.assertNotEqual(a_		,   0						)
							self.assertEqual(a_		,   a_						)
							self.assertEqual(a_		,   len(a_						)						)
							self.assertEqual(a_		,   all_size_a + len(a_						)						)

							lowerCAmelCase  :     Union[str, Any]       						= tokenizer.encode(
							    ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"		,   add_special_tokens=a_						)

							self.assertGreaterEqual(len(a_						)		,   6						)
							self.assertGreater(tokens[0]		,   tokenizer.vocab_size - 1						)
							self.assertGreater(tokens[0]		,   tokens[1]						)
							self.assertGreater(tokens[-3]		,   tokenizer.vocab_size - 1						)
							self.assertGreater(tokens[-3]		,   tokens[-4]						)
							self.assertEqual(tokens[0]		,   tokenizer.eos_token_id						)
							self.assertEqual(tokens[-3]		,   tokenizer.pad_token_id						)



	def 	_lowerCamelCase     ( self						):
			pass



	def 	_lowerCamelCase     ( self						):
			pass



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Union[str, Any]       						= self.get_tokenizer()

			lowerCAmelCase  :     Union[str, Any]       						= tokenizer.tokenize("This is a test"						)
			# fmt: off
			self.assertListEqual(a_		,   [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"]						)
			# fmt: on

			self.assertListEqual(
			    tokenizer.convert_tokens_to_ids(a_						)		,   [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]		,   )

			lowerCAmelCase  :     Tuple       						= tokenizer.tokenize("I was born in 92000, and this is falsé."						)
			self.assertListEqual(
			    a_		,   [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]						)

			lowerCAmelCase  :     str       						= tokenizer.convert_tokens_to_ids(a_						)
			# fmt: off
			self.assertListEqual(a_		,   [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26]						)
			# fmt: on

			lowerCAmelCase  :     Tuple       						= tokenizer.convert_ids_to_tokens(a_						)
			self.assertListEqual(
			    a_		,   [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."]						)





	@slow
	def 	_lowerCamelCase     ( self						):
			# Use custom sequence because this tokenizer does not handle numbers.
			lowerCAmelCase  :     Optional[Any]       						= [
			    "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
			    "general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
			    "Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
			    "models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
			    "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
			    "conditioning on both left and right context in all layers.",
			    "The quick brown fox jumps over the lazy dog.",
			]

			# fmt: off
			lowerCAmelCase  :     List[Any]       						= {
			    "input_ids": [
			        [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
			        [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
			        [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
			    ],
			    "attention_mask": [
			        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
			        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
			        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
			    ]
			}
			# fmt: on

			self.tokenizer_integration_test_util(
			    expected_encoding=a_		,   model_name="microsoft/speecht5_asr"		,   revision="c5ef64c71905caeccde0e4462ef3f9077224c524"		,   sequences=a_		,   )










'''simple docstring'''






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 		lowerCamelCase	(   _A							):
	snake_case_   = ["image_processor", "tokenizer"]
	snake_case_   = "LayoutLMv3ImageProcessor"
	snake_case_   = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



	def __init__( self		,   a_=None		,   a_=None		,   **a_						):
			lowerCAmelCase  :     Union[str, Any]       						= None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
					    " instead."		,   a_		,   )
					lowerCAmelCase  :     Tuple       						= kwargs.pop("feature_extractor"						)

			lowerCAmelCase  :     str       						= image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError("You need to specify an `image_processor`."						)
			if tokenizer is None:
					raise ValueError("You need to specify a `tokenizer`."						)

			super().__init__(a_		,   a_						)



	def __call__( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = True		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = 0		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = False		,   a_ = True		,   a_ = None		,   **a_		,   ):
			# verify input
			if self.image_processor.apply_ocr and (boxes is not None):
					raise ValueError(
					    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."						)

			if self.image_processor.apply_ocr and (word_labels is not None):
					raise ValueError(
					    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."						)

			# first, apply the image processor
			lowerCAmelCase  :     str       						= self.image_processor(images=a_		,   return_tensors=a_						)

			# second, apply the tokenizer
			if text is not None and self.image_processor.apply_ocr and text_pair is None:
					if isinstance(a_		,   a_						):
							lowerCAmelCase  :     Union[str, Any]       						= [text]  # add batch dimension (as the image processor always adds a batch dimension)
					lowerCAmelCase  :     int       						= features["words"]

			lowerCAmelCase  :     List[Any]       						= self.tokenizer(
			    text=text if text is not None else features["words"]		,   text_pair=text_pair if text_pair is not None else None		,   boxes=boxes if boxes is not None else features["boxes"]		,   word_labels=a_		,   add_special_tokens=a_		,   padding=a_		,   truncation=a_		,   max_length=a_		,   stride=a_		,   pad_to_multiple_of=a_		,   return_token_type_ids=a_		,   return_attention_mask=a_		,   return_overflowing_tokens=a_		,   return_special_tokens_mask=a_		,   return_offsets_mapping=a_		,   return_length=a_		,   verbose=a_		,   return_tensors=a_		,   **a_		,   )

			# add pixel values
			lowerCAmelCase  :     Tuple       						= features.pop("pixel_values"						)
			if return_overflowing_tokens is True:
					lowerCAmelCase  :     Optional[int]       						= self.get_overflowing_images(a_		,   encoded_inputs["overflow_to_sample_mapping"]						)
			lowerCAmelCase  :     str       						= images

			return encoded_inputs



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
			lowerCAmelCase  :     Optional[Any]       						= []
			for sample_idx in overflow_to_sample_mapping:
					images_with_overflow.append(images[sample_idx]						)

			if len(a_						) != len(a_						):
					raise ValueError(
					    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
					    F''' {len(a_						)} and {len(a_						)}'''						)

			return images_with_overflow



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.batch_decode(*a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   *a_		,   **a_						):
			return self.tokenizer.decode(*a_		,   **a_						)



	@property
	def 	_lowerCamelCase     ( self						):
			return ["input_ids", "bbox", "attention_mask", "pixel_values"]



	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,   a_		,   )
			return self.image_processor_class





	@property
	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,   a_		,   )
			return self.image_processor



